Speaker

Biography

Abstract

Juan Abellan

Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.

Tipping point sensitivity analysis for time-to-event: a case study in belimumab

In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.

Kevin Ding

Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.

Practical use of Tipping Point Analysis in regulatory submissions of clinical trials

The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

Speaker

Biography

Abstract

Juan Abellan

Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.

Tipping point sensitivity analysis for time-to-event: a case study in belimumab

In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.

Kevin Ding

Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.

Practical use of Tipping Point Analysis in regulatory submissions of clinical trials

The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

Speaker

Biography

Abstract

Juan Abellan

Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.

Tipping point sensitivity analysis for time-to-event: a case study in belimumab

In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.

Kevin Ding

Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.

Practical use of Tipping Point Analysis in regulatory submissions of clinical trials

The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

Speaker

Biography

Abstract

Juan Abellan

Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.

Tipping point sensitivity analysis for time-to-event: a case study in belimumab

In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.

Kevin Ding

Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.

Practical use of Tipping Point Analysis in regulatory submissions of clinical trials

The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

Speaker

Biography

Abstract

Juan Abellan

Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.

Tipping point sensitivity analysis for time-to-event: a case study in belimumab

In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.

Kevin Ding

Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.

Practical use of Tipping Point Analysis in regulatory submissions of clinical trials

The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

Speaker

Biography

Abstract

Juan Abellan

Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.

Tipping point sensitivity analysis for time-to-event: a case study in belimumab

In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.

Kevin Ding

Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.

Practical use of Tipping Point Analysis in regulatory submissions of clinical trials

The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.